Multi-machine learning model based on radiomics features to predict prognosis of muscle-invasive bladder cancer

作者：Bin Wang, Zijian Gong, Pei-Chun Su, Guanghao Zhen, Tao Zeng, Yinquan Ye · 发表于：BMC Cancer · 年份：2025 · DOI：10.1186/s12885-025-14279-6 · 被引用次数：4 · 研究领域：Bladder and Urothelial Cancer Treatments、Radiomics and Machine Learning in Medical Imaging、Cholangiocarcinoma and Gallbladder Cancer Studies

OBJECTIVE: This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features. MATERIALS AND METHODS: A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model's performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis. RESULTS: Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767-0.952) for the training group, 0.850, 95% CI (0.705-0.995) for the validation group, and 0.700, 95% CI (0.520-0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704-0.913) for the training group, 0.895, ...